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Course Criteria
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1.00 Credits
This is a colloquium that allows fourth-year students to learn about engineering design, innovation, teamwork, technical communication, and project management in the context of their two-semester systems capstone design project.? With respect to their capstone project, students define and scope their project, structure an interim report about the project, and give an oral presentation to the class.? In addition, students study methods of effective time management and prepare presentations of their 5-year career plans.(Y) Prerequisites & Notes Prerequisite: Fourth-year standing in systems engineering. Credits: 1
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0.50 - 3.00 Credits
Detailed study of a selected topic determined by the current interest of faculty and students. Offered as required.
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3.00 Credits
Detailed study of a selected topic determined by the current interest of faculty and students. Offered as required. (IR) Prerequisites & Notes Prerequisite: As specified for each offering. Credits: 3
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3.00 Credits
To learn basic aspects of human factors in the design of information support systems. We will cover: (1) basic human performance issues (physiology, memory, learning, problem-solving, human error), (2) the user interface design process (task analysis, product concept, functional requirements, prototype, design, and testing.) Students will gain basic skills in the analysis and design of human-machine systems through in-class exercises and two course projects. The course is also designed to help you practice different communication skills (interviewing, written analysis, and oral presentation). (Y) Credits: 3
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3.00 Credits
Independent study or project research under the guidance of a faculty member. Offered as required. (IR) Prerequisites & Notes Prerequisite: As specified for each offering. Credits: 1 - 4 Technology Management and Policy
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1.00 - 6.00 Credits
Independent study or project research under the guidance of a faculty member. Offered as required.
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3.00 Credits
This course is an introduction to the theory of the industrial organization (from a game-theoretic perspective) and its applications to industries with strong engineering content (electricity, telecommunications, software and hardware, etc.). Topics include: congestion pricing in networks, pricing and efficiency in electricity markets, planned obsolescence in software development, “networks” effects and the dynamics of technology adoption.
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3.00 Credits
An integrated introduction to systems methodology, design, and management. An overview of systems engineering as a professional and intellectual discipline, and its relation to other disciplines, such as operations research, management science, and economics. An introduction to selected techniques in systems and decision sciences, including mathematical modeling, decision analysis, risk analysis, and simulation modeling. Elements of systems management, including decision styles, human information processing, organizational decision processes, and information system design for planning and decision support. Emphasizes relating theory to practice via written analyses and oral presentations of individual and group case studies.
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3.00 Credits
Provides an introduction to the problems encountered when integrating large systems, and also presents a selection of specific technologies and methodologies used to address these problems. Includes actual case-studies to demonstrate systems integration problems and solutions. A term project is used to provide students with the opportunity to apply techniques for dealing with systems integration.
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3.00 Credits
This course is an introduction to theory and application of mathematical optimization. The goal of this course is to endow the student with a) a solid understanding of the subject’s theoretical foundation and b) the ability to apply mathematical programming techniques in the context of diverse engineering problems. Topics to be covered include a review of convex analysis (separation and support of sets, application to linear programming), convex programming (characterization of optimality, generalizations), Karush-Kuhn-Tucker conditions, constraint qualification and Lagrangian duality. The course closes with a brief introduction to dynamic optimization in discrete time.
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